System and method for detecting poor quality in 3D reconstructions

ABSTRACT

A system and method for detecting poor quality images in an optical tomography system includes an acquisition apparatus for acquiring a set of pseudo-projection images of an object having a center of mass, where each of the set of pseudo-projection images is acquired at a different angle of view. A reconstruction apparatus is coupled to receive the pseudo-projection images, for reconstruction of the pseudo-projection images into 3D reconstruction images. A quality apparatus is coupled to receive the 3D reconstruction images and operates to detect of selected features that characterize poor quality reconstructions.

FIELD OF THE INVENTION

The present invention relates generally to analysis of medical imagingdata, and, more particularly, to detecting poor quality inthree-dimensional (3D) reconstructions in a biological cell imager.

BACKGROUND OF THE INVENTION

3D tomographic reconstructions require projection images as input. Aprojection image assumes that an object of interest is translucent to asource of exposure such as a light source transmitted through the objectof interest. The projection image, then, comprises an integration of theabsorption by the object along a ray from the source to the plane ofprojection. Light in the visible spectrum is used as a source ofexposure in optical projection tomography.

In the case of producing projections from biological cells, the cellsare typically stained with hematoxylin, an absorptive stain thatattaches to proteins found in cell chromosomes. Cell nuclei areapproximately 15 microns in diameter, and in order to promotereconstructions of sub-cellular features it is necessary to maintainsub-micron resolution. For sub-micron resolution, the wavelength of theilluminating source is in the same spatial range as the biologicalobjects of interest. This can result in undesirable refraction effects.As a result a standard projection image cannot be formed. To avoid theseundesirable effects, as noted above, the camera aperture is kept openwhile the plane of focus is swept through the cell. This approach toimaging results in equal sampling of the entire cellular volume,resulting in a pseudo-projection image. A good example of an opticaltomography system has been published as United States Patent ApplicationPublication 2004-0076319, on Apr. 22, 2004, corresponding to U.S. Pat.No. 7,738,945 issued Jun. 15, 2010, to Fauver, et al. and entitled“Method and Apparatus for Pseudo-Projection Formation for OpticalTomography.” U.S. Pat. No. 7,738,945 is incorporated herein byreference.

An optical tomography system may advantageously employ scores forclassifying objects of interest, for example, to detect lung cancer inits pre-invasive and treatable stage. In order to do so with accuracyand reliability, the classification scores must be based on good quality3D reconstruction images of the objects being classified. One example ofan optical tomography system is being built by VisionGate, Inc. of GigHarbor Wash., assignee of this application, is under the trademark“Cell-CT™.” In one aspect, the Cell-CT™ optical tomography systememploys scores, designed to provide an indication of lung cancer in itspre-invasive and treatable stage.

While it is generally understood that poor quality 3D reconstructionsmay adversely affect classification results in optical tomographysystems, an automated system for detecting such poor quality 3Dreconstructions has been lacking until now. The system and methoddisclosed herein provides, for the first time, a solution for detectionof poor quality 3D reconstructions useful for an optical tomographysystem, for example.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features ofthe claimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

A system and method for detecting poor quality images in an opticaltomography system is presented. The system includes an acquisition meansfor acquiring a set of projection images of an object having a center ofmass, where each of the set of projection images is acquired at adifferent angle of view. A reconstruction means is coupled to receivethe projection images, for reconstruction of the projection images into3D reconstruction images. A quality means for classification of the 3Dreconstruction images uses selected features that characterize poorquality reconstructions.

BRIEF DESCRIPTION OF THE DRAWINGS

While the novel features of the invention are set forth withparticularity in the appended claims, the invention, both as toorganization and content, will be better understood and appreciated,along with other objects and features thereof, from the followingdetailed description taken in conjunction with the drawings, in which:

FIG. 1 shows a highly schematic view of an optical projection tomographysystem including a quality score classifier.

FIG. 2A and FIG. 2B show slices from reconstructions wherepseudo-projections are in good focus and poor focus respectively.

FIG. 3A and FIG. 3B show slices from reconstructions wherepseudo-projections are in good alignment and poor alignmentrespectively.

FIG. 4 shows a slice from a reconstructed cell where the cell boundaryand corresponding segmentation boundary are shown.

FIG. 5A and FIG. 5B show slices from reconstructions wherepseudo-projections are in good alignment and poor alignmentrespectively.

FIG. 6A and FIG. 6B show a fixed focal plane slice and a reconstructionslice for a good quality reconstruction.

FIG. 7A and FIG. 7B show a fixed focal plane slice and a reconstructionslice for a poor quality reconstruction.

FIG. 8A shows a comparison of center of mass trend with a curve fitusing a cosine function for a good quality reconstruction.

FIG. 8B shows a comparison of center of mass trend with a curve fitusing a cosine function for a poor quality reconstruction.

FIG. 9 shows quality classifier ROC curve where sensitivity measures thedetection accuracy for poor reconstructions.

DESCRIPTION OF THE PREFERRED EMBODIMENT

The following disclosure describes several embodiments and systems forimaging an object of interest. Several features of methods and systemsin accordance with example embodiments of the invention are set forthand described in the figures. It will be appreciated that methods andsystems in accordance with other example embodiments of the inventioncan include additional procedures or features different than those shownin figures.

Example embodiments are described herein with respect to biologicalcells. However, it will be understood that these examples are for thepurpose of illustrating the principles of the invention, and that theinvention is not so limited. Additionally, methods and systems inaccordance with several example embodiments of the invention may notinclude all of the features shown in these figures. Throughout thefigures, like reference numbers refer to similar or identical componentsor procedures.

Unless the context requires otherwise, throughout the specification andclaims which follow, the word “comprise” and variations thereof, suchas, “comprises” and “comprising” are to be construed in an open,inclusive sense that is as “including, but not limited to.”

Reference throughout this specification to “one example” or “an exampleembodiment,” “one embodiment,” “an embodiment” or various combinationsof these terms means that a particular feature, structure orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present disclosure. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

Generally as used herein the following terms have the following meaningswhen used within the context of optical tomography processes:

-   -   “Capillary tube” has its generally accepted meaning and is        intended to include transparent microcapillary tubes and        equivalent items with an inside diameter generally of 500        microns or less.    -   “Depth of field” is the length along the optical axis within        which the focal plane may be shifted before an unacceptable        image blur for a specified feature is produced.    -   “Object” means an individual cell, item, thing, particle or        other microscopic entity.    -   “Pseudo-projection” or “pseudo-projection image” includes a        single image representing a sampled volume of extent larger than        the native depth of field of a given set of optics. One concept        of a pseudo-projection is taught in Fauver '744.    -   “Specimen” means a complete product obtained from a single test        or procedure from an individual patient (e.g., sputum submitted        for analysis, a biopsy, or a nasal swab). A specimen may be        composed of one or more objects. The result of the specimen        diagnosis becomes part of the case diagnosis.    -   “Sample” means a finished cellular preparation that is ready for        analysis, including all or part of an aliquot or specimen.

As used in this specification, the terms “processor” and “computerprocessor” encompass a personal computer, a microcontroller, amicroprocessor, a field programmable object array (FPOA), a digitalsignal processor (DSP), an application-specific integrated circuit(ASIC), a field programmable gate array (FPGA), a programmable logicarray (PLA), or any other digital processing engine, device orequivalent including related memory devices, transmission devices,pointing devices, input/output devices, displays and equivalents.

Referring now to FIG. 1 a highly schematic view of an optical projectiontomography system including a quality score classifier is shown. Cells15 are suspended in an index of refraction matching gel 12 contained ina capillary tube 18. Pressure 10 is applied to the gel 12 to move thecells into the optical path of a high-magnification microscope includingan objective lens 5. The objective lens 5 is scanned or vibrated by, forexample, a (not shown) piezo-electric element. The capillary tube 18 ispositioned to be scanned by the vibrating objective lens 5. Anillumination source 20 operates to illuminate objects, such asbiological cells passing through the field of view of the objective lens5. An image sensor 25 is located to acquire images transmitted from theobjective lens 5. A plurality of pseudo-projection images, hereexemplified by pseudo-projection images 22A, 22B and 22C are acquired bythe image sensor 25 at varying angles of view as presented by therotating capillary tube 18. The plurality of pseudo-projection imagesare then passed to a reconstruction processor 35 for producing 3Dimages. A quality score classifier 36 is coupled to the reconstructionprocessor 35 to receive the reconstructed 3D images. The quality scoreclassifier, in one embodiment, then classifies the received images asacceptable or of poor quality and not acceptable for further analysis.

In the example, the plurality of pseudo-projection images, hereexemplified by pseudo-projection images 22A, 22B and 22C are shownacquired at angles of 0°, 90° and 180° respectively. It will beunderstood that these are merely examples and that the number ofpseudo-projection images actually acquired will typically be severalhundred images. The reconstruction processor 35 may be of the type asdescribed in Fauver et al. referenced hereinabove. The quality scoreclassifier may, for example, advantageously assign numerical scores tothe reconstructed 3D images where the numerical scores have valuesscaled to represent degrees of quality of the images. In otherembodiments the quality score classifier may simply sort poor qualityimages from other images.

Having described the major components of an optical tomography systemincluding a quality score classifier, it is now considered useful to anunderstanding of the invention to describe an example embodiment ofoperation of such a system. Taken in a substantially chronologicalorder, an example of operation may include the following functions.

-   -   1. A specimen for examination is processed to remove        non-diagnostic elements and is fixed and stained.    -   2. The specimen is then suspended in a gel medium. The cells in        gel mixture are then inserted into a glass micro-capillary tube        18 of approximately 50μ inner diameter 16.    -   3. Pressure is applied to the gel to move the cells into the        optical path 14 of a high-magnification microscope.    -   4. Once the cells are in place the tube is rotated to permit        capture of 500 high resolution images of the desired object        taken over 360 degrees of tube rotation. These images are        simulations of projection images created by integrating the        light from the objective lens as the objective scans the        nucleus. The simulated projection or pseudo-projection images        thus represent the entire nuclear content in a single image,        taken from a single perspective.    -   5. Pseudo-projection images are processed to correct for        residual noise and motion artifacts.    -   6. The corrected pseudo projections are processed using filtered        back projection to yield a 3D tomographic representation of the        cell.    -   7. Based on the tomographic reconstruction, features are        computed that are used, for example, to detect cells with        characteristics of indicative of cancer and its precursors.        These features may be used in a classifier whose output        designates the likelihood that object under investigation        belongs in a specified class, such as a cancer cell.

Among other things, good quality classification depends on good quality3D reconstructions in step 6. Issues governing quality arise fromdetrimental effects that may be introduced by the operation of a givenoptical tomography system and characteristics relating to deficientcorrection of random cell motion occurring during image capture. Ifcells are not properly in focus in the set of pseudo-projections or ifthe cell moves off the camera frame during capture, the resultingreconstruction will not be ideal. In a likewise fashion, if propercorrections for the random motions arising during image capture are notmade, then the various features of the cell will not reinforce eachother in the reconstruction, thus compromising reconstruction quality.

Poor quality images may result in distorted reconstructions entering theclassification stream, producing unpredictable results reflected inincorrect or distorted classification scoring. Therefore, poor qualityreconstructions need to be detected to ensure the integrity ofclassification. A method for detecting poor quality reconstructions incases where, for example, pseudo-projection images were not collected inan ideal way, when registration was not successful, or for other reasonsaffecting image quality, is described in detail herein sufficient forone skilled in the art to make and use the invention.

As described further herein, detection of poor quality reconstructionsmay be carried out by various methods including poor quality detectionbased on features describing streaking in reconstruction, poor qualitydetection based on a comparison between fixed focal plane andreconstructed slice, poor quality detection using parameters of cosinefitting to center of mass trends and the like. It has been observed thatstreaking may have various causes. Image quality issues due to poorfocus and random motions affecting cell alignment have similar streakingeffects on reconstructions.

Referring now to FIG. 2A and FIG. 2B, slices from reconstructions wherepseudo-projections are in good focus and poor focus respectively areshown. Now addressing effects of poor focus, it has been observed asshown in FIG. 2B, that occasionally, cells are not well focused across asub-set of the pseudo-projections. In poorly focused pseudo-projections,morphology is usually blurred, producing blurred image features such aslobe 202. When back-projected, such blurred image features do notideally align with the same features found in well-focusedpseudo-projections from the same set. This lack of alignment creates astreaking effect in the reconstruction.

Referring now to FIG. 3A and FIG. 3B, slices from reconstructions wherepseudo-projections are in good alignment and poor alignment respectivelyare shown. Poor alignment due to random motions of the cell occurringduring image capture must be corrected for post-acquisition in software.One such system is described in US Patent Publication No. 20080285827,published Nov. 20, 2008, for U.S. Pat. No. 7,835,561 issued Nov. 16,2010 to Meyer et al. entitled, “Method For Image Processing AndReconstruction Of Images For Optical Tomography,” which is incorporatedherein by reference.

In some circumstances, the correction algorithm does not converge to anappropriate solution and poor alignment is observed in the acquired setof corrected pseudo-projections that are used as input to thefiltered-backprojection algorithm. As a result, cell morphology does notreinforce in the backprojection. The effect of poor alignment is similarto that of poor focus. Lack of good quality alignment produces streakingin the reconstruction. FIG. 3A shows a slice from a reconstruction froma well focused and well aligned cell. Note the crisp boundary 302describing the cell and nucleus. FIG. 3B shows a slice from areconstruction created where a sub-set of the pseudo-projections werewell focused but poorly aligned with the other pseudo-projections. Notethat cell and nuclear boundaries are not crisp and that a streakartifact is observed in the background of the reconstruction.

Comparing FIG. 2B and FIG. 3B, it can be observed that poor focus andpoor registration produce similar effects on the reconstruction. Theseeffects may be recognized in order to detect a poor qualityreconstruction by characterizing the voxels in the reconstruction thatare not associated with the cell. Performing the recognition based onthe background allows for an algorithm that is not as stronglyinfluenced by the diverse biology that one observes from cell to cell.The process begins with a segmentation algorithm to separate the cellfrom the background.

For some applications, segmentation development may be initiated withannotations of reconstructions made by hand drawn cell boundaries. Theseboundaries serve as a reference to guide development. The resultingsegmentation algorithm includes identification of a threshold, selectedfor the particular cell under examination. In one example, thresholdselection follows a procedure wherein a cell segmentation program firstselects fifteen slices near the center of the reconstruction. With eachslice a range of thresholds is applied and an area derivative and asecond derivative is computed for each. To select a threshold for eachslice, a negative second derivative is located at a threshold higherthan the maximum area derivative. A global threshold is chosen using apercentile of the selected slice thresholds. Finally, the largest objectis kept, and any holes in it are filled using digital techniques.

Referring now to FIG. 4, a slice from a reconstructed cell where thecell boundary and corresponding segmentation boundary are shown. Usingthe segmentation techniques described herein a computed segmentationmask was developed to correspond to this boundary. The resultingsegmentation algorithm produced the boundary 402. The segmentation maskwas applied to the reconstruction by setting all voxel values in thereconstruction that are also within the segmentation mask to a value of255. Those skilled in the art will recognize that voxel and pixel lightintensity values for reconstructions and image slices typically vary inbrightness on a scale from 0 to 255, but that other scales may also beemployed without departing from the scope and spirit of the invention.

Referring now to FIG. 5A and FIG. 5B, slices from reconstructions wherepseudo-projections are in good alignment and poor alignment respectivelyare shown. The pseudo projections include a cell 100, where the cell hasbeen segmented and the background voxels have been amplified to fill thegrey scale range. FIG. 5A and FIG. 5B respectively show the result offurther processing of the images of FIG. 3A and FIG. 3B after a mask hasbeen applied and the background has been equalized so that the histogramfor the background extends across the available grey scale range. Notethe prominent streak artifact 102 for the image associated with poorregistration. The streak artifact 102 may be characterized by computinga set of features on those voxels in the reconstruction that areassociated with the background of the reconstruction. Table 1 provides alist of features that may advantageously be employed for characterizingthe streak effect.

TABLE I Feature Type Description Histogram As seen in FIG. 5A and FIG.5B, there is a greater variance in background voxels for the poorlyaligned cell 102 versus the well aligned cell 100. Therefore, featuresthat characterize various statistics on background voxels may beemployed for detection of poor quality of reconstructions. Suchstatistics may advantageously include: mode, mean, median, variance,coefficient of variance, skewness, kurtosis, various percentiles of thehistogram - 10th, 40th percentile, etc. Spatial As seen by comparingFIG. 5A and FIG. 5B, the images exhibit Frequency a different pattern inthe spatial frequencies of the two different reconstructed slices.Therefore, procedures whose values characterize the spatial frequenciesof the reconstructed slices may be employed for detection of poorquality of reconstructions. Useful procedures may advantageously includeFourier transforms, FFT, wavelet transform, etc. Texture TextureFeatures characterize the distribution of grey scale values in thebackground. Texture methods are based on mathematical morphology. Twomethods may be especially important for quality detection. First, runlength features characterize the length of a gray scale run. These aretypically represented in a histogram. Run-length features arestatistical moments computed on the run-length histogram, such as mean,variance, mode, etc., Second, blur residue features characterize textureby computing a difference image using morphological opening or closingon the background and subtracting it from the original, masked image.Statistically- based features may be then computed on the backgroundvoxels in the difference image for different choices of structureelement used for opening and/or closing.

Another technique for assessing reconstruction quality is to comparereconstruction slices with their corresponding fixed focal plane slices.So long as they are well focused, fixed focal plane slices should befree of whatever distortions were introduced into the reconstructionduring image capture or processing. Therefore, these images form anexcellent reference to judge reconstruction quality. Referring now toFIG. 6A and FIG. 6B, fixed focal plane and reconstruction slices for agood quality reconstruction are shown respectively. FIG. 6A shows afixed focal plane image and FIG. 6B shows a slice from thereconstruction that best matches it from a good quality reconstruction.Similarly, FIG. 7A shows a fixed focal plane image and FIG. 7B shows aslice from the reconstruction that best matches it from a poor qualityreconstruction.

Features derived to judge good quality of reconstruction are formed bycreating a difference image between the fixed focus and reconstructionslice images. In contrast with the above features of Table 1, differenceimage features are computed for those voxels that are associated withthe cell. Low average difference for the portion of the imagescontaining the cell reflects good quality of reconstruction.

Another useful method for detection of poor quality images employsparameters of cosine fitting to center of mass trends. As indicated byFIG. 1, data collection on an optical tomography system proceeds bymoving objects, for example, cells, into position under the objectivelens and spinning the capillary tube to collect the set ofpseudo-projections. When viewed from a specific perspective the centerof mass of the cell moves up and down in a cosine pattern when plottedagainst capillary angle of rotation. Poor registration occurs when thegrey-scale mass is not conserved across all pseudo-projections. Whenthis occurs, the trend in the center of mass often deviates from acosine. Detection of poor quality reconstruction may therefore bepotentially accomplished by fitting the trend in center of mass with acosine function and characterizing the error of the fit. Specificfeatures used for detection include the absolute and radius normalizedmaximum deviation, and root mean square error (RMSE) between center ofmass cosine fit and trend.

Referring now to FIG. 8A, an example of a comparison of center of masstrend 802 with fit using a cosine 804 for a good quality reconstructionis shown. FIG. 8B shows an example of a comparison of center of masstrend 806 with fit using a cosine 808 for a poor quality reconstruction.In both graphs the horizontal axes represent the pseudo-projectionnumber. The vertical axes represent the center of mass position inmicrons. Note the negligible deviation between fit 802 with trend 804for the good reconstruction to the point that the curves essentiallycoincide, and the more substantial fit-trend deviation between lines 806and 808 for the poor reconstruction.

With respect to the example of FIG. 8A the following curve fitstatistics apply:

-   -   Fit error mean=0.102 μm,    -   Fit error standard deviation=0.071 μm,    -   Fit error maximum (absolute)=0.28 μm,    -   Fit error maximum delta=0.08 μm,    -   Radius to object center=19.797 μm,    -   X offset=−0.222 μm,    -   Phase=0.114 degrees,    -   Relative Deviation in frequency=0.147%, and    -   Linear drift in X=0 μm.

With respect to the example of FIG. 8B the following curve fitstatistics apply:

-   -   Fit error mean=1.051 μm,    -   Fit error standard deviation=0.756 μm,    -   Fit error maximum (absolute)=2.35 μm,    -   Fit error maximum delta=4.541 μm,    -   Radius to object center=8.993 μm,    -   X offset=−0.14 μm,    -   Phase=−19.033 degrees,    -   Relative deviation in frequency=0.356%, and    -   Linear drift in X=0.005 μm.        Note that the error statistics, such as the fit error mean, for        the poor quality reconstruction are an order of magnitude larger        than the error statistics for a good quality reconstruction.

Referring now to FIG. 9, a quality classifier ROC curve is shown. Usingthe above described features and expert identification of poorreconstruction quality, a classifier was created whose output optimallycorresponds to the expert identification. This correspondence may besummarized using a receiver operator characteristic (ROC) curve.Sensitivity is represented on the vertical axis ranging from 0.0 to 1.0.Here sensitivity measures the detection accuracy for poorreconstructions. Specificity is represented on the horizontal axis alsoranging from 0.0 to 1.0. Those skilled in the art having the fullbenefit of this disclosure will understand how to build a qualityscoring classifier using the selected features identified hereinabove.

While specific embodiments of the invention have been illustrated anddescribed herein, it is realized that numerous modifications and changeswill occur to those skilled in the art. It is therefore to be understoodthat the appended claims are intended to cover all such modificationsand changes as fall within the true spirit and scope of the invention.

What is claimed is:
 1. A system for detecting poor quality images in anoptical tomography system comprising: acquisition means for acquiring aset of pseudo-projection images of an object having a center of mass,where each of the set of pseudo-projection images is acquired at adifferent angle of view; reconstruction means, coupled to receive thepseudo-projection images, for reconstruction of the pseudo-projectionimages into 3D reconstruction images; and quality means forclassification of the 3D reconstruction images using selected featuresthat characterize poor quality reconstructions wherein the set ofpseudo-projection images present a center of mass trend for the objectand the selected features are calculated from a comparison of the centerof mass trend with a cosine function fitting the trend in the center ofmass.
 2. The system of claim 1 wherein the selected features describestreaking in the 3D reconstruction images.
 3. The system of claim 1wherein the selected features include difference image features that areformed by creating a difference image between a fixed focus image and areconstruction slice image.
 4. The system of claim 3 wherein thedifference image features are computed for those voxels that areassociated with the cell including an average difference for the portionof the images containing the cell.
 5. The system of claim 1 wherein thecalculation of the selected features includes measuring an error betweenthe cosine function and the center of mass trend.
 6. The system of claim5 wherein the selected features include the absolute and radiusnormalized maximum deviation, and root mean square error (RMSE) betweenthe cosine function and the center of mass trend.
 7. The system of claim1 wherein the selected features are selected from the group consistingof histogram statistics, texture features and spatial frequencyfeatures.
 8. The system of claim 1 wherein the object comprises abiological cell.
 9. A method for detecting poor quality images in anoptical tomography system comprising: operating the optical tomographysystem to acquire a set of pseudo-projection images of an object havinga center of mass, where each of the set of pseudo-projection images isacquired at a different angle of view; transmitting the set ofpseudo-projection images to a processor for reconstructing thepseudo-projection images into 3D reconstruction images; and operatingthe processor to classify the 3D reconstruction images using selectedfeatures that characterize poor quality reconstructions wherein theselected features include difference image features that are formed bycreating a difference image between a fixed focus image and areconstruction slice image.
 10. The method of claim 9 wherein theselected features describe streaking in the 3D reconstruction images.11. The method of claim 9 wherein the difference image features arecomputed for those voxels that are associated with the cell including anaverage difference for the portion of the images containing the cell.12. The method of claim 9 wherein the set of pseudo-projection imagespresent a center of mass trend for the object and the selected featuresare calculated from a comparison of the center of mass trend with acosine function fitting the trend in the center of mass.
 13. The methodof claim 12 wherein the calculation of the selected features includesmeasuring an error between the cosine function and the center of masstrend.
 14. The method of claim 13 wherein the selected features includethe absolute and radius normalized maximum deviation, and root meansquare error (RMSE) between the cosine function and the center of masstrend.
 15. The method of claim 9 wherein the selected features areselected from the group consisting of histogram statistics, texturefeatures and spatial frequency features.
 16. The method of claim 9wherein the object comprises a biological cell.
 17. A system fordetecting poor quality images in an optical tomography system where aset of cells are suspended in an index of refraction matching gelcontained in a rotating capillary tube, where pressure is applied to thegel to move the cells into the optical path of a microscope including anobjective lens that is scanned through the capillary tube while anillumination source operates to illuminate cells passing through thefield of view of the objective lens and where the optical tomographysystem includes an image sensor located to acquire pseudo-projectionimages transmitted from the objective lens, where each of the set ofpseudo-projection images is acquired at a different angle of view, thesystem comprising: a reconstruction processor, coupled to receive a setof pseudo-projection images of each of the set of cells, each cellhaving a center of mass, where the reconstruction processor creates atleast one 3D reconstruction image; a quality score classifier coupled toreceive the at least one 3D reconstruction image, where the qualityscore classifier scores selected features in the at least one 3Dreconstruction image; wherein the selected features describe streakingin reconstruction; wherein the set of pseudo-projection images present acenter of mass trend for the cell and the selected features furtherinclude comparison features calculated from a comparison of the centerof mass trend with a cosine fit curve; wherein the selected featuresinclude difference image features that are formed by creating adifference image between a fixed focus image and a reconstruction sliceimages; and wherein the difference image features are computed forvoxels that are associated with the cell including an average differencefor the portion of the images containing the cell.
 18. The system ofclaim 17 wherein the calculation of the selected features includesselected features includes measuring an error between the cosinefunction and the center of mass trend.
 19. The system of claim 18wherein the selected features include the absolute and radius normalizedmaximum deviation, and root mean square error (RMSE) between the cosinefunction and the center of mass trend.
 20. The system of claim 17wherein the selected features are selected from the group consisting ofhistogram statistics, texture features and spatial frequency features.